arXiv:2606. 26125v1 Announce Type: cross Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
arXiv:2606. 17684v1 Announce Type: cross Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications.
Subgraph Filtering for Fair Graph Neural Networks (SF‑GNN) is a lightweight, architecture‑agnostic framework that reduces structural bias in GNNs by identifying and filtering bias‑prone edges during message passing. It combines sensitive homophily with structural propagation amplifiers such as hub participation and triadic closure to detect problematic edges, then applies stochastic edge filtering to downweight or remove them while preserving the rest of the graph. Experiments on five benchmark datasets demonstrate that SF‑GNN consistently improves fairness while maintaining competitive predictive performance, achieving a better fairness–accuracy trade‑off than recent fairness‑aware GNN baselines.
Graph neural networks (GNNs) can exhibit unfair behavior even when sensitive attributes are excluded from node features, because graph topology and message passing propagate group-correlated signals u...
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
The paper introduces QEF-GT-AdamW, a communication‑efficient and outage‑resilient algorithm for decentralized wireless federated learning. It combines gradient tracking, AdamW adaptive optimization, and dual‑stream biased quantization with error feedback to reduce communication payloads while mitigating non‑IID data effects. The method includes a local fallback strategy for unreliable broadcasts and provides convergence guarantees under compressed, unreliable wireless communication, demonstrating improved robustness and accuracy‑communication trade‑offs on heterogeneous MNIST and CIFAR‑10 datasets.